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    Optical detection element and GOI device for ultra-small on-chip optical sensing, and manufacturing method of the same
    111.
    发明授权
    Optical detection element and GOI device for ultra-small on-chip optical sensing, and manufacturing method of the same 有权

    公开(公告)号：US11860109B2

    公开(公告)日：2024-01-02

    申请号：US17680469

    申请日：2022-02-25

    Applicant: Korea Advanced Institute of Science and Technology

    Inventor： Sanghyeon Kim ,  Jinha Lim ,  Joonsup Shim

    IPC: G01N21/95 ,  H01L21/762 ,  G02B6/122

    CPC classification number: G01N21/9501 ,  G02B6/122 ,  H01L21/7624

    Abstract: Various embodiments relate to an optical detection element and GOI (Ge-on-insulator) device for ultra-small on-chip optical sensing, and a manufacturing method of the same. According to various embodiments, the optical detection element and the GOI device may be implemented on a GOI structure comprising a germanium (Ge) layer, and the GOI device may be implemented to have an optical detection element. Specifically, the GOI device may include a GOI structure with a waveguide region comprising a germanium layer, a light source element configured to generate light for the waveguide region, and at least one optical detection element configured to detect light coming from the waveguide region. At least one slot configured to collect light from the light source element may be formed in the germanium layer in the waveguide region. The light source element may generate light so as to be coupled to the germanium layer in the waveguide region. The optical detection element may detect heat generated as light is propagated from the germanium layer.

    INFORMATION PROCESSING APPARATUS AND INFORMATION PROCESSING METHOD
    115.
    发明公开
    INFORMATION PROCESSING APPARATUS AND INFORMATION PROCESSING METHOD 审中-公开

    公开(公告)号：US20230396564A1

    公开(公告)日：2023-12-07

    申请号：US18454477

    申请日：2023-08-23

    Applicant: TOYOTA JIDOSHA KABUSHIKI KAISHA ,  Korea Advanced Institute of Science and Technology

    Inventor： Chunghan LEE ,  Jinwoo PARK ,  Byoungkwon CHOI ,  Dongsu HAN

    IPC: H04L47/80 ,  H04L47/70 ,  H04L47/83

    CPC classification number: H04L47/805 ,  H04L47/823 ,  H04L47/83

    Abstract: An information processing apparatus comprises a controller. The controller is configured to execute: acquiring a plurality of datasets, each of the datasets being configured with a combination of training data and a correct answer label; and implementing machine learning of an estimation model using the acquired plurality of datasets, wherein the training data includes workload information about an application constructed based on a microservice architecture and resource use information about resources used for each of components included in the application, in a learning target environment, the correct answer label is configured to show a true value of quality of service of the application, and the machine learning comprises training the estimation model such that, for each of the datasets, an estimated value of the quality of service calculated with the estimation model based on the training data corresponds to the true value shown by the correct answer label.

    3D POINT CLOUD-BASED DEEP LEARNING NEURAL NETWORK ACCELERATION APPARATUS AND METHOD
    119.
    发明公开
    3D POINT CLOUD-BASED DEEP LEARNING NEURAL NETWORK ACCELERATION APPARATUS AND METHOD 审中-公开

    公开(公告)号：US20230376756A1

    公开(公告)日：2023-11-23

    申请号：US18199995

    申请日：2023-05-22

    Applicant: Korea Advanced Institute of Science and Technology

    Inventor： Hoi Jun YOO ,  Dong Seok IM

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: Disclosed is a 3D point cloud-based deep learning neural network acceleration apparatus including a depth image input unit configured to receive a depth image, a depth data storage unit configured to store depth data derived from the depth image, a sampling unit configured to sample the depth image in units of a sampling window having a predetermined first size, a grouping unit configured to generate a grouping window having a predetermined second size and to group inner 3D point data by grouping window, and a convolution computation unit configured to separate point feature data and group feature data, among channel-direction data of 3D point data constituting the depth image, to perform convolution computation with respect to the point feature data and the group feature data, to sum the results of convolution computation by group grouped by the grouping unit, and to derive the final result.

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